Desirability Function Approach Research Articles

Immersion cooling of a cylindrical battery module: An optimum configuration using CFD, NSGA-II and desirability function approach

Immersion cooling offers promising advantages for cylindrical battery modules, particularly in applications where compact design, efficient thermal management, and enhanced safety are critical factors. In this study, the geometric, thermal, and dynamic parameters of an immersion-cooled battery module with 14 NCM cylindrical cells are analyzed using CFD methods. The developed battery model is experimentally validated, establishing the constraints through numerical analysis of the impact of design parameters on response values. The design parameters are optimized using a multi-objective optimization technique. The study models the battery module with mineral oil cooling and examines the impact of each parameter on variables such as maximum battery module temperature (Tmax), pressure drop (ΔP), maximum temperature variance (ΔTmax), and battery module mass index (Im). A Design of Experiment set, created with Central Composite Design (CCD), is solved using CFD methods, and quadratic correlation equations are derived using backward regression. The regression models for Tmax, ΔTmax, ΔP, and Im show high accuracy with R2 values close to 1.0. Optimization is performed using the entropy weight-based composite desirability function approach, yielding optimum values for cell center spacing (t), coolant mass flow rate (ṁc), and coolant inlet temperature (Ti) at 20.606 mm, 1.5 kg/s, and 23 °C, respectively. The optimized design sets are validated through CFD analysis, showing a maximum deviation of 4.27 %. Reducing t boosts thermal performance but lowers hydraulic performance, while the reverse occurs for ṁc. As Ti decreases, effective cooling improves, but temperature uniformity suffers.

Tribological behavior of a Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy and prediction through response surface methodology based mathematical modeling

In the present work, a novel Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy (CCA) was developed using the vacuum arc melting technique. The as-cast CCA underwent a two-stage heat treatment process. In the first stage of heat treatment (HT 1), the alloy was heated to 900˚C. Subsequently, in the second stage of heat treatment (HT 2), the HT 1 samples were annealed at various temperatures, including 700˚C, 900˚C, and 1100˚C, for 20 h. The microstructure and mechanical response of the as-cast, HT 1 and HT 2 (annealed) samples were thoroughly investigated. The phase evolution, microstructure, and chemical composition were analyzed using XRD and SEM-EDS. The XRD analysis revealed major solid solution BCC phases (BCC 1 and BCC 2) with a small amount of Laves phase; however, the amount of Laves phase increased with increase in annealing temperature. The microhardness and elastic modulus of the CCA specimens were evaluated using instrumented micro-indentation. The CCA annealed at 1100˚C exhibits the highest microhardness and elastic modulus, with values of 5.94 ± 0.38 GPa and 124.40 ± 7.17 GPa, respectively. Response surface methodology (RSM) was used to develop a mathematical model aimed at predicting tribological characteristics, specifically the specific wear rate (SWR) and coefficient of friction (COF). RSM proposes a quadratic model to represent the mathematical relationship between input parameters for evaluating SWR and COF. The desirability function approach is employed to optimize input parameters to minimize both SWR and COF. The optimized values of SWR and COF are 6.87 × 10−4 mm3/N.m and 0.30 under a 27.52 N load, 2.86 Hz oscillation frequency, and 1100˚C annealing temperature. Surface topography analysis of the worn surface was evaluated using SEM and a profilometer to understand the wear mechanism and surface characteristics.

Multi-response optimization in laser processing technologies by applying desirability function approach and response surface methodology based on grey relation analysis

Laser processing technologies are among the leading industry technologies for efficient and economical processing of a wide spectrum of engineering materials. The determination of optimal parameter settings with respect to multiple and opposite process performances is of great practical importance in laser processing technologies. This paper discusses, analyzes, and compares two main approaches for multi-response optimization (MRO) in engineering, that is, desirability function approach (DFA) and grey relation analysis (GRA), while solving five case studies, covering different laser processing technologies, such as cutting, drilling, welding, micro-channeling, and cladding. In each case study the MRO solutions obtained with two competitive approaches were assessed using the relative target deviation (RTD) criterion. Analysis of the obtained MRO solutions and comparative analysis with results from previous studies indicate that the integrated RSM-GRA provide similar and comparable solutions with the hybrid Taguchi method and GRA, while DFA proved to be a better and more promising approach for solving MRO in laser processing technologies. Some specific features, limitations, and possibilities of MRO approaches were also discussed.

Technology development of novel autogenous double pulse tungsten insert gas welding technique to evaluate the depth of penetration, micro segregation via machine learning and desirability function approach

The present research investigates the effect of process parameters on the microstructure, micro segregation, and material properties of the Autogenous Double Pulse Tungsten Inert Gas Welding (ADP-TIG) of Inconel 625. The optimization of the process parameter was evaluated by Response surface Methodology (RSM) Box Behnken method. In this article, the effects of the weld-bead geometry, weld centre (WC), weld interface (WI), and heat affected zone (HAZ) were evaluated through macro morphology, microstructure, SEM/EDAX, XRD, and micro hardness. The outcomes indicate that by employing a combination of high and low frequency pulsing in ADP-TIG, it is possible to regulate not only the dimensions of the weld-bead and penetration depth but also the HAZ. The ADP-TIG process has a low heat input, resulting in finer grains in the microstructure, reduced secondary phases, and a narrower HAZ. SEM images of alloy 625 confirms the depiction of fine equiaxed dendrites. The ADP-TIG process reduces secondary phase formation due to a reduction in Mo and Nb alloying elements. EDAX results also show that the micro segregation in the weld centre (WC) and weld interface (WI) is very less in the dendritic core region because of the lower heat Input. The XRD result confirms the presence of NbzNi, Cr₂Nb, Ni8Nb, Fe2Mo3 and FeNi phases. The maximum microhardness values in WC and WI are 290 HV and 280 HV, respectively. The impacts of ADP-TIG welding technique specifications on factors such as weld bead geometry, WC, WI, and HAZ have been thoroughly examined across a range of process parameters. Further the process parameters of ADP-TIG were optimized using a desirability function to achieve defect-free welds, followed by experimental validation. The desirability function results were then compared with a machine learning (ML) approach. The investigation aimed to assess ML algorithms' predictive capability for weldment outcomes. From the ML modelling results, Support Vector Machine (SVM) using the Radial Basis Function (RBF) kernel was identified as the superior strategy for accurately predicting Heat input, Mo segregation, and Nb segregation. Additionally, Gaussian process regression (GPR) with an RBF kernel was recommended for predicting the Width to Depth Ratio.

Valorization of bottom ash in concrete: serviceability, microstructural and sustainability characterization

The present study investigated the synergistic influence of bottom ash as a Portland cement (PC) and natural fine aggregate (NFA) replacement in concrete. Coal bottom ash (CBA) is a heavy ash that settles at bottom of combustion chamber of a thermal power plant and was grinded (GCBA) for two hours prior to replacing 10-30% PC whereas CBA was used in raw form to replace 25% and 50% NFA. The mechanical properties along with durability properties (accelerated carbonation and chloride penetration) were studied after 28 day and 90 days curing. Non-destructive tests were also performed to check the quality of CBA based concrete. Microstructural characterization was conducted using various techniques like XRD, SEM and FTIR. The concrete with 20% GCBA and 25% CBA (G20C25) reported best performance in terms of parameters studied owing to pozzolanic reactivity of GCBA and filler effect of fine CBA. The microstructural investigations also validated the findings and trends observed of experimental results. Well fitted mathematical models were derived and optimisation was carried out using desirability function approach. Multi-objective optimization recommended 21.80% GCBA and 24.17% CBA as the optimum amount resulting in significant reduction of 19.19% and 18.19% in carbon footprints and eco-costs compared to control mix.

Study on adaptive regulation based on water supply-demand system structure and water use desirability under extreme drought

The frequent occurrence of drought events has changed the structure of the water supply-demand system, seriously hindering the sustainable development of society. This study aims to address the challenges of unclear impact of the system structure changes on water use desirability (WUD), as well as the regional water resources adaptive regulation. First, the structure of the water supply-demand system was analyzed based on the system theory. Then water supply priority (WSP) and water use compression (WUC) were considered to set combined regulation scenarios, which respectively characterize the structure of the water supply and demand systems. The MIKE BASIN/NAM water resources regulation coupling model was constructed to simulate the water use process under different scenarios. The desirability function approach was introduced to quantify the impact of system structure changes on WUD, and ultimately the adaptive regulation strategies were proposed with the goal of the highest WUD. Taking Chuxiong Prefecture in Yunnan, China as the study area, the validity of the model was verified, and the main results show that (1) t he optimal WSP in Chuxiong Prefecture was: urban domestic > rural domestic > industry > agriculture; (2) agriculture was identified as the most appropriate water user for compressing; (3) it was recommended that the adaptive regulation measure was to adjust the WSP in Dayao, Mouding and Wuding; and change the WUC in Yongren and Yao'an. In Nanhua, Chuxiong, Shuangbai and Lufeng, the adaptive regulation measure was to joint adjustment of WSP and WUC. These results clarify the impact of the system structure changes on WUD in Chuxiong Prefecture, identifying the optimal WSP and optimal WUC user under extreme drought, and propose water resources adaptive regulation suggestions for each county. In addition, the water resources adaptive regulation technology proposed in this study can provide scientific basis for water resources allocation under future climate change.

Optimizing hydrothermal synthesis of titanium dioxide nanotubes: Doehlert method and desirability function approach

Optimizing hydrothermal synthesis of titanium dioxide nanotubes: Doehlert method and desirability function approach

Modelling of Proximate Composition of Amaranth, Sorghum, Pumpkin and Sunflower Flour Blends using Response Surface Methodology

Blends of cereals and legumes have gained attention especially in complementary nutrition. Optimization of the production of composite flour from defatted amaranth, sorghum, defatted pumpkin and defatted sunflower flour using D-optimal mixture design of Design Expert software with the levels for amaranth (40-60 %), sorghum (10-30 %), pumpkin (20-40 %), and sunflower flour (3-10 %), respectively was carried out. The responses were proximate composition. Run 5 and 14 recorded the highest crude protein (4.08 %) and total ash content (5.71 %) while run 7 and 10 had the highest fibre content (4.08 %.), respectively. The model terms were significant (p≤0.05) for the proximate composition of the blends with R2 values of 0.93, 0.92, 0.96, 0.90, 0.91, and 0.90 respectively for moisture, protein, fat, ash, fibre, and carbohydrate. The optimal blend from the numerical optimization through the desirability function approach were 42.46% amaranth, 10.00% sorghum, 40.00% pumpkin, and 7.54%. In conclusion, composite flour from amaranth, sorghum, pumpkin and sunflower flour have acceptable proximate composition in terms of nutritional quality necessary for the production of nutrient-dense food products capable of addressing issues of malnutrition.

Shear strength characterization and sustainability assessment of coal bottom ash concrete

The present study investigated the synergistic influence of coal bottom ash (CBA) on the shear strength of concrete. CBA was milled for 2, 6, and 10 h to form grinded CBA (GCBA). “L” shaped specimens were prepared with 10%–30% GCBA and 25%–50% CBA as alternative of Portland cement and natural fine aggregates. Concrete containing 20% GCBA (grinded for 6 h) and 25% CBA reported the highest shear strength owing to pozzolanic reactiveness and filler action. X-ray diffraction, scanning electron microscopy–energy-dispersive spectroscopy and Fourier transform infrared also supported the experimental outcomes. Well fitted mathematical models were derived followed by optimization using desirability function approach recommending 5.71 h of grinding, 26.27% GCBA, and 36.69% CBA as the optimum amount for its successful utilization in concrete. This approach further leads to significant reduction of about 22% in carbon footprints and eco-costs in comparison to conventional concrete.

Experimental investigation to assess the surface integrity in WEDM of Al-based hybrid composite material

Aluminum-based hybrid composites are used in various engineering applications due to their unique properties, such as high strength, good wear resistance, excellent corrosion resistance, and low density. Aluminum metal matrix (AMMC) composites reinforced with ceramic particles can be more difficult to machine than traditional aluminum alloys because of their increased hardness and abrasiveness. Wire electric discharge machining is a non-traditional machining process that uses a thin metal wire, typically made of molybdenum, brass or tungsten to cut through a workpiece using electrical discharge sparks. This process is useful for precision machining of complex shapes and profiles in hard or difficult to machine materials. WEDM is a commonly used process for cutting Al-SiC-ZrO2 HAMMCs due to their hardness and difficulty in machining using conventional techniques. The present study examines the relationship between machining parameters like pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed (WF) and material removal rate (MRR) and surface roughness ( Ra) for Al-SiC-ZrO2 in WEDM. The Box-Beckham Design was used to plan the experiments, and response surface method was employed to develop the models. The pulse duration, TOFF, IP, and quadratic terms of [Formula: see text] and [Formula: see text] are the most influential factors of the MRR. The TON, IP and interaction term (TON × IP), and [Formula: see text] were the most significant factors for Ra. Multi-response optimization of process parameters was obtained using the desirability function approach. Furthermore, the machined samples were analyzed for surface integrity using SEM techniques. The prediction from this model validated the confirmation results.

Recycled rubber wastes-based polymer composites with flame retardancy and electrical conductivity: Rational design, modeling and optimization

Polymer recycling techniques experience a maturity period of design and application. Rubbers comprise a high proportion of polymer wastes, highly flammable and impossible to re-melt. Polymer composites based on ground tire rubber (GTR) and ethylene-vinyl acetate copolymer (EVA) containing carbon black (CB) (1–50 phr), with variable EVA/GTR weight composition (10/90, 25/75, 50/50, 75/25 and 90/10), and processing temperature (Low: 100 °C and High: 200 °C) were designed applying Design of Experiments (DOE) approach of Optimal Design. The properties and performance features were experimentally evaluated. The tensile strength (TS) and elongation at break (EB) were optimized using Desirability Function (DF) approach. A wide fluidity window (Labeled POOR, GOOD, and EXCELLENT) and mechanical properties were observed. Overall, higher values of EB were assigned to samples processed at 200 °C. Cubic regression modeling and DF optimization of TS and EB indicated unlikely that one expect a TS ≥ 3 MPa, while EB values more than 500% were likely regardless of CB content for EVA-rich composites. The electrical properties of CB/EVA/GTR samples were examined by impedance spectroscopy technique. An interesting relationship was observed between the DC conductivity and the EVA/GTR ratio and processing temperature. GTR-rich samples showed much higher conductivity than EVA-dominated samples, attributed to the presence of CB in the rubber waste, which, together with the added CB, was able to create conduction paths for the transported electrons. Higher processing temperature of 200 °C improved dispersion of the added CB, i.e. a more even distribution of the conductive phase in the matrix. The uniform and evenly dispersed domains of particles were detected by SEM images for highly CB loaded composites. Surprisingly, 50/50 EVA/GTR composites were resilient against flame, while thermally stable ones in TGA measurements were highly CB loaded ones.

Bionanofactory for green synthesis of collagen nanoparticles, characterization, optimization, in-vitro and in-vivo anticancer activities

Collagen nanoparticles (collagen-NPs) are promising biological polymer nanoparticles due to their exceptional biodegradability and biocompatibility. Collagen-NPs were bio-fabricated from pure marine collagen using the cell-free supernatant of a newly isolated strain, Streptomyces sp. strain NEAA-3. Streptomyces sp. strain NEAA-3 was identified as Streptomyces plicatus strain NEAA-3 based on its cultural, morphological, physiological properties and 16S rRNA sequence analysis. The sequence data has been deposited under accession number OR501412.1 in the GenBank database. The face-centered central composite design (FCCD) was used to improve collagen-NPs biosynthesis. The maximum yield of collagen-NPs was 9.33 mg/mL with a collagen concentration of 10 mg/mL, an initial pH of 7, an incubation time of 72 h, and a temperature of 35 °C. Using the desirability function approach, the collagen-NPs biosynthesis obtained after FCCD optimization (9.53 mg/mL) was 3.92 times more than the collagen-NPs biosynthesis obtained before optimization process (2.43 mg/mL). The TEM analysis of collagen-NPs revealed hollow sphere nanoscale particles with an average diameter of 33.15 ± 10.02 nm. FTIR spectra confirmed the functional groups of the collagen, collagen-NPs and the cell-free supernatant that are essential for the efficient capping of collagen-NPs. The biosynthesized collagen-NPs exhibited antioxidant activity and anticancer activity against HeP-G2, MCF-7 and HCT116 cell lines. Collagen-NPs assessed as an effective drug loading carrier with methotrexate (MTX), a chemotherapeutic agent. The TEM analysis revealed that the average size of MTX-loaded collagen-NPs was 35.4 ± 8.9 nm. The percentages of drug loading (DL%) and encapsulation efficiency (EE%) were respectively 22.67 and 45.81%.

Optimization of CNC-FSSW parameters for dissimilar spot welding of AA6061 aluminium alloy and mild steel using Taguchi based desirability function approach

Optimization of CNC-FSSW parameters for dissimilar spot welding of AA6061 aluminium alloy and mild steel using Taguchi based desirability function approach

A Box-Behnken design-based chemometric approach to optimize the sono-photodegradation of hydroxychloroquine in water media using the Fe(0)/S2O82-/UV system.

The huge utilization of hydroxychloroquine in autoimmune infections led to an abnormal increment in its concentration in wastewater, which can pose a real risk to the environment, necessitating the development of a pretreatment technique. To do this, we are interested in researching how hydroxychloroquine degrades in contaminated water. The main goal of this investigation is to optimize the operating conditions for the sono-photodegradation of hydroxychloroquine in water using an ultrasound-assisted Fe(0)/ /UV system. To get adequate removal of HCQ, a chemometric method based on the Box-Behnken design was applied to optimize the influence of the empirical parameters selected, including Fe(0) dose, concentration, pH, and initial HCQ concentration. The quadratic regression model representing the HCQ removal rate (η(%)) was evolved and validated by ANOVA. The optimal conditions as a result of the above-mentioned trade-off between the four input variables, with η(%) as the dependent output variable, were captured using RSM methodology and the composite desirability function approach. For HCQ full decomposition, the optimal values of the operating factors are as follows: dose, 194.309 mg/L; Fe(0) quantity, 198.83 mg/L; pH = 2.017, and HCQ initial dose of 296.406 mg/L. Under these conditions, the HCQ removal rate, achieved after 60 min of reaction, attained 98.95%.

Lead extraction from food samples by combined cloud point-micro solid phase extraction

An economic strategy was investigated for extraction of Pb(II) by merging cloud point extraction (CPE) and micro solid phase extraction (μSPE) using montmorillonite (MMT) as a natural sorbent. In this context, Pb(II) was adsorbed onto MMT as 4-(pyridyl-2-azo)-resorcinol (PAR) complex and transferred into micellar phase of Triton X-114. Subsequently, Pb(II) were eluted from MMT by 0.5 mol L−1 of HNO3 and quantified by graphite furnace atomic absorption spectrometry (GFAAS). The central composite design of response surface methodology was used to study the effects of some experimental parameters such as pH, MMT amount, surfactant concentration, and temperature while the desirability function approach was utilized to define the best conditions for Pb(II) extraction. The calibration curve was linear from 0.02 to 10.0 µg L−1 of and an enrichment factor of 95.6 was obtained for 50.0 mL sample. The limit of detection for was 0.006 µg L−1 and the relative standard deviations for five replicate analysis of 1.0 µg L−1 and 5.0 µg L−1 of Pb(II) were 2.8% and 1.5%, respectively. The method was assessed for its environmental friendliness using the analytical AGREEnness calculator. The applicability of the method were evaluated by determination of Pb(II) in certified reference materials and food samples.

Quality characteristic of instant rice produced using microwave‐assisted hot air drying

AbstractBackground and ObjectivesIt is of utmost importance for the instant rice processing sector to reassess its methodologies to enhance the overall quality of instantized rice products. This study aimed to analyze how the combination of microwave (MW) drying and convective heated air drying influences the quality of instantized rice. Novel approaches for drying precooked rice were explored, involving the utilization of an industrial MW operating at a frequency of 915 MHz with treatment conditions set at 4 kW power for 8 min. This was followed by two distinct convective oven drying strategies, each lasting 4 min. Specifically, stepwise convective hot air drying involved progressively decreasing temperatures, while constant temperature convective hot air drying maintained a consistent temperature. The convective hot air‐drying temperatures ranged from 160°C to 230°C, while the constant temperature options were either 160°C or 200°C. Both strategies encompassed three relative humidity levels (20%, 40%, and 60%). The impact of these techniques on the quality attributes of a newly developed long‐grain (LG) hybrid rice variety, RT 7521 FP, was assessed.FindingsThe drying approach and percentage RH significantly impacted the percentage points of moisture removed, with both their p values smaller than .01. For water activity, the main effect of the drying approach was significant because the p values of its hypothesis test were smaller than .05. The results showed that increasing the percentage of RH increases the mean water activity under the constant medium, constant high, and stepwise medium approaches. MW drying followed by a Constant high approach under 40% RH had a higher rehydration ratio value of instantized rice than all other drying treatments. The second highest rehydration ratio was observed in MW drying, followed by stepwise high treatments. None of the effects of the factors of RH and drying approach were significant for the rehydration ratio because their p values were all larger than .05. For bulk density, the main effects of RH and the drying approach were not significant since their p values were larger than .05. The drying temperature had significant impacts on textural parameters such as hardness, adhesiveness, cohesiveness, gumminess, chewiness, and resilience, except springiness. RH impacted both adhesiveness and resilience. All parameters measured, including percentage RH and drying approach, did not significantly impact the rehydration ratio, color, and bulk density of treated instant rice samples (p > .05).ConclusionsPrediction profiler for multiattribute optimization using a desirability function approach from John's Macintosh Project Pro 17 statistical software was used to identify the best setting of RH and the drying approach that maximizes the percentage of moisture content removed, minimizes water activity, and reaches an adhesiveness and resilience values close to −4.615 and 0.6722, respectively. The best setting for RH was identified as 20%, and the best drying approach was stepwise high. Under these settings, the predicted mean percentage of moisture removed, water activity, adhesiveness, and resilience of the models were 44.43%, 0.478, −9.02, and 0.5, respectively. Therefore, these settings are recommended to achieve rice samples with attractive moisture content removed, water activity, adhesiveness, and resilience.Significance of StudyThe study has provided crucial information on the instantizing potential of a new variety of hybrid rice and the feasibility of combining a 915 MHz industrial MW and convective hot air to dry and obtain instant rice with improved quality attributes, including high rehydration ratios for shorter cooking times, lighter weight, and longer shelf life. The information generated from this study may be useful to guide decisions on drying conditions for industrial processing of instantized rice with premium quality.

Investigation on the influence of FSP parameters on TIG welded dissimilar aluminum joints using RSM and desirability function approach

The present study aimed to explore the cumulative influence of friction stir processing (FSP) parameters, viz., tool spindle speed, traverse speed, and tool tilt angle, on the ultimate tensile strength (TS), ultimate flexural strength (FS), microhardness (MH), and wear rate (WR) of tungsten inert gas (TIG) welded dissimilar AA6061-T6 and AA7075-T6 alloy joints. A face-cantered central composite design (FCCCD) of response surface methodology (RSM) was adopted to design the experiments. Analysis of variance (ANOVA) was employed to identify the significant factors and interaction effects. Multi-response optimization was performed using the desirability function approach to identify parameters that maximize TS, FS, and MH and minimize WR. A confirmation study under optimal conditions were also carried out. FSP significantly improved the TIG joint properties by producing more homogeneous and refined grains (average grain size of 7.75 µm) without any weld defects. The TS, FS, and MH of TIG welds have increased by 66.87%, 61.95%, and 69.01%, respectively, while the WR decreased by 37.87% after FSP. The fractography revealed that TIG welds lead to a brittle failure mode, and the FSP + TIG welds showed a ductile failure mode. The tool spindle speed was the most influential factor affecting joint properties. With increased spindle speed, the TS, FS, and MH of the joint increased, while the WR decreased. At a spindle speed of 1300 rpm, a traverse speed of 50.75 mm/min, and a tilt angle of 1.76°, the optimum TS of 271.89 MPa, the FS of 298.03 MPa, the MH of 122.23 HV, and the WR of 77.48 µm were observed.

Investigation of machining rate and surface roughness in wire EDM of Al6063/WC/ZrO2 composite using response surface methodology

The aim of the current work is to explore the machinability behavior of Al6063/WC/ZrO2 metal matrix composites (MMC) produced through a stir casting process through wire electrical discharge machining (WEDM) process. In order to examine the effects of process parameters such as voltage (V), pulse on time (Ton) and pulse off time (Toff) on material removal rate (MRR) and surface roughness (SR), the experiments were conducted by adapting Response Surface Methodology (RSM) in conjunction with the central composite design (CCD). A second-order regression model has been developed to predict the response parameters and an analysis of variance (ANOVA) was performed to validate the significance of the models. Using the desirability function approach, the parameters were set for the highest MRR and the minimum SR value. The prediction was within a tolerable average error range. A total of 19 sets of tests were developed to obtain six possible solutions. The most suitable solution among these six has been found by a confirmatory experiment. The results indicate that voltage and its interactions have significantly affected both the responses than Ton and Toff. Overall, it can be said that the study of the Al6063/WC/ZrO2 MMCs using WEDM process parameters demonstrated improved casting and machining qualities.

Integrating DFT and machine learning for the design and optimization of sodium alginate-based hydrogel adsorbents: Efficient removal of pollutants from wastewater

This study presents a novel approach to design and optimize a sodium alginate-based hydrogel (SAH) for efficient adsorption of the model water pollutant methylene blue (MB) dye. Utilizing density functional theory (DFT) calculations, sodium alginate-g-poly (acrylamide-co-itaconic acid) was identified with the lowest adsorption energy (Eads) for MB dye among 14 different clusters. SAHs were prepared using selected monomers and sodium alginate combinations through graft co-polymerization, and swelling studies were conducted to optimize grafting conditions. Advanced characterization techniques, including FTIR, XRD, XPS, SEM, EDS, and TGA, were employed, and the process was optimized using statistical and machine learning tools. Screening tests demonstrated that Eads serves as an effective predicting indicator for adsorption capacity (qe) and MB removal efficiency (RRMB,%), with reasonable agreement between Eads and both responses under given conditions. Process modeling and optimization revealed that 5 mg of selected SAH achieves a maximum qe of 3244 mg g−1 at 84.4% RRMB under pH 8.05, 98.8 min, and MB concentration of 383.3 mg L−1, as identified by the desirability function approach. Moreover, SAH effectively eliminated various contaminants from aqueous solutions, including sulfasalazine (SFZ) and dibenzothiophene (DBT). MB adsorption onto selected SAH was exothermic, spontaneous, and followed the pseudo-first-order and Langmuir-Freundlich isotherm models. The remarkable ability of SAH to adsorb MB is attributed to its well-designed structure predicted through DFT and optimal operational conditions achieved by AI-based parametric optimization. By integrating DFT-based computations and machine-learning tools, this study contributes to the efficient design of adsorbent materials and optimization of adsorption processes, also showcasing the potential of SAH as an efficient adsorbent for the abatement of aqueous pollution.

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

The growing popularity of soy proteins among vegans and vegetarians, owing to their high protein content and widespread availability, has led to scientific studies on its various extraction methods mainly on ultrafiltration. This research employed artificial neural network (ANN) and Box-Behnken design (BBD) methodologies to predict the process parameters of ultrafiltration for the preparation of soy protein. Using BBD, the optimum process parameters of ultrafiltration were identified via the desirability function approach. The optimized permeate flux was 11.13 litres per hour (LPH) and 85.52% protein content in retentate. The identified ideal process parameters for ultrafiltration to achieve maximal protein retention encompassed a 10 kDa membrane module, a transmembrane pressure of 117 kPa (17 PSI), a volume concentration ratio of 3.5, diafiltration set at 1, and a flow rate of 65% of the pump capacity, exhibiting an absolute percent error value of 2.81. Employing these refined process parameters, the predicted value for protein retentate stood at 80.49%. The predictive accuracy of the model achieved an impressive 99.61% for protein retention. The ANN model effectively predicted the optimal ultrafiltration conditions, resulting in maximal protein retention and a protein content accuracy of 96.41% and 99.61%, respectively.